The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt

Ahmed Elbeltagi; Muhammad Rizwan Aslam; Anurag Malik; Behrouz Mehdinejadiani; Ankur Srivastava; Amandeep Singh Bhatia; Jinsong Deng

doi:10.1016/j.scitotenv.2020.140770

The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt

Sci Total Environ. 2020 Nov 15:743:140770. doi: 10.1016/j.scitotenv.2020.140770. Epub 2020 Jul 6.

Authors

Ahmed Elbeltagi¹, Muhammad Rizwan Aslam², Anurag Malik³, Behrouz Mehdinejadiani⁴, Ankur Srivastava⁵, Amandeep Singh Bhatia⁶, Jinsong Deng⁷

Affiliations

¹ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt. Electronic address: ahmed_elbeltagi@zju.edu.cn.
² College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: rizwanaslam00786@gmail.com.
³ Punjab Agricultural University, Regional Research Station, Bathinda 151001, Punjab, India. Electronic address: anuragmalik_swce2014@rediffmail.com.
⁴ Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran. Electronic address: b.mehdinejad@uok.ac.ir.
⁵ School of Engineering, The University of Newcastle, Callaghan, NSW 2308, Australia. Electronic address: ankursrivastava117@gmail.com.
⁶ Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. Electronic address: amandeep.9807@chitkara.edu.in.
⁷ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: jsong_deng@zju.edu.cn.

PMID: 32679501
DOI: 10.1016/j.scitotenv.2020.140770

Abstract

Spatial-temporal information of different water resources is essential to rationally manage, sustainably develop, and optimally utilize water. This study focused on simulating future water footprint (WF) of two agronomically important crops (i.e., wheat and maize) using deep neural networks (DNN) method in Nile delta. DNN model was calibrated and validated by using 2006-2014 and 2015-2017 datasets. Moreover, future data (2022-2040) were obtained from three Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, and incorporated into DNN prediction set. The findings showed that determination-coefficient between historical-predicted crop evapotranspiration (ET_c) varied from 0.92 to 0.97 for two crops. The yield prediction values of wheat-maize deviated within the ranges of -3.21% to 3.47% and -4.93% to 5.88%, respectively. Based on the ensemble of RCP, precipitation was forecasted to decease by 667.40% and 261.73% in winter and summer in western as compared to eastern, respectively, which will ultimately be dropped to 105.02% and 60.87%, respectively parallel to historical. Therefore, the substantial fluctuations in precipitation caused an obvious decrease in green WF of wheat (i.e., 24.96% and 37.44%) in western and eastern, respectively. Additionally, for maize, it induced a 103.93% decrease in western and an 8.96% increase in eastern. Furthermore, increasing ET_c by 8.46% and 12.45% gave rise to substantially increasing (i.e., 8.96% and 17.21%) in western for wheat-maize compared to the east, respectively. Likewise, grey wheat-maize WF findings reveals that there was an increase of 3.07% and 5.02% in western as compared to -14.51% and 12.37% in eastern. Hence, our results highly recommend the optimal use of the eastern delta to save blue-water by 16.58% and 40.25% of total requirements for wheat-maize in contrast to others. Overall, the current research framework and results derived from the adopted methodology will help in optimal planning of future water under climate change in the agricultural sector.

Keywords: Climate change; Climate projection; Deep neural networks (DNN); Sustainable water; Water footprint.